LEACH-RLC: Enhancing IoT Data Transmission with Optimized Clustering and Reinforcement Learning

Wireless Sensor Networks (WSNs) play a pivotal role in enabling Internet of Things (IoT) devices with sensing and actuation capabilities. Operating in remote and resourceconstrained environments, these IoT devices face challenges related to energy consumption, crucial for network longevity. Existing clustering protocols often suffer from high control overhead, inefficient cluster formation, and poor adaptability to dynamic network conditions, leading to suboptimal data transmission and reduced network lifetime. This paper introduces Low-Energy Adaptive Clustering Hierarchy with Reinforcement Learning-based Controller (LEACH-RLC), a novel clustering protocol designed to address these limitations by employing a Mixed Integer Linear Programming (MILP) approach for strategic selection of Cluster Heads (CHs) and node-to-cluster assignments. Additionally, it integrates a Reinforcement Learning (RL) agent to …

BibTeX citation

                        @article{JuradoFafoutis2025,
 author = {Jurado Lasso, Fabian Fernando and Jurado, Jesus Fabian and
 Fafoutis, Xenofon},
 title = {LEACH-RLC: {Enhancing} {IoT} {Data} {Transmission} with
 {Optimized} {Clustering} and {Reinforcement} {Learning}},
 journal = {IEEE Internet of Things Journal},
 date = {2025-03},
 doi = {10.1109/JIOT.2025.3552126}
}

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LEACH-RLC: Enhancing IoT Data Transmission with Optimized Clustering and Reinforcement Learning

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